CoChat
Sign Up
Cited by 214

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Restoration of energy homeostasis by SIRT6 extends healthy lifespan

Roichman, A., Elhanati, S., Aon, M. A., Abramovich, I., Di Francesco, A., Shahar, Y., Avivi, M. Y., Shurgi, M., Rubinstein, A., Wiesner, Y., Shuchami, A., Petrover, Z., Lebenthal-Loinger, I., Yaron, O., Lyashkov, A., Ubaida-Mohien, C., Kanfi, Y., Lerrer, B., Fernández-Marcos, P. J., Serrano, M., Gottlieb, E., de Cabo, R., Cohen, H. Y.·2021·Nature Communications
DOI 10.1038/s41467-021-23545-7PMC PMC8163764
Open this paper in CoChat — free account

Abstract

Aging leads to a gradual decline in physical activity and disrupted energy homeostasis. The NAD + -dependent SIRT6 deacylase regulates aging and metabolism through mechanisms that largely remain unknown. Here, we show that SIRT6 overexpression leads to a reduction in frailty and lifespan extension in both male and female B6 mice. A combination of physiological assays, in vivo multi-omics analyses and 13 C lactate tracing identified an age-dependent decline in glucose homeostasis and hepatic glucose output in wild type mice. In contrast, aged SIRT6-transgenic mice preserve hepatic glucose output and glucose homeostasis through an improvement in the utilization of two major gluconeogenic precursors, lactate and glycerol. To mediate these changes, mechanistically, SIRT6 increases hepatic gluconeogenic gene expression, de novo NAD + synthesis, and systemically enhances glycerol release from adipose tissue. These findings show that SIRT6 optimizes energy homeostasis in old age to delay frailty and preserve healthy aging.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-021-23545-7

Cites

20 of 20 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 SIRT6 regulates lifespan and healthspan of C57BL/6JOlaHsd mice of both sexes. a Kaplan–Meier survival curves for male, female, and combined sexes of WT (n = 52 males, n = 50 females), SIRT1-tg (n = 47 males, n = 30 females), SIRT6-tg (n = 51 males, n = 41 females), and SIRT1 + 6-tg (n = 47 males, n = 44 females) mice. p-values for SIRT6-tg vs. WT mice are shown and were derived from two-tailed log-rank calculations. Median lifespan for each genotype is shown in parentheses. b–d Neoplasia (b), cancer (c), and gastrointestinal adenoma (d) incidence at the age of 25 months. For (b–d), n = 16 mice per genotype, a mixture of both sexes. Two-tailed Fisher’s exact test; *p < 0.05 vs. WT. Exact p-values are reported in Supplementary Table 1. e Respiratory exchange ratio (RER) averaged over 3 days in young and old WT and SIRT6-tg mice from either sex measured every half-hour. Three-way ANOVA with repeated measures with age, genotype, and time as variables. Sidak’s post hoc; young WT (* in blue) or old SIRT6 (* in green) vs. old WT. n = 8 mice per group, except for young SIRT6 where n = 7 mice. f Spontaneous wheel running activity of males at the ages of 8 months (n = 8 mice per genotype) and 15 months (n = 9 mice for WT, SIRT1, SIRT6, n = 7 mice for SIRT1 + 6). Two-way ANOVA with time and genotype as variables. g Exercise ability on treadmill in young and old males. Two-way ANOVA with Sidak’s post hoc. n = 6 mice per group, except for young WT where n = 5 mice. *p < 0.05, **p < 0.01, ***p < 0.001. In (e) and (f), values are shown as mean ± SEM. In (g), box extends from the 25th to 75th percentiles, line in the middle of the box is the median and whiskers go down to the smallest value and up to the largest. For (e–g), exact p-values are reported in the Source Data file.
Fig. 1 SIRT6 regulates lifespan and healthspan of C57BL/6JOlaHsd mice of both sexes. a Kaplan–Meier survival curves for male, female, and combined sexes of WT (n = 52 males, n = 50 females), SIRT1-tg (n = 47 males, n = 30 females), SIRT6-tg (n = 51 males, n = 41 females), and SIRT1 + 6-tg (n = 47 males, n = 44 females) mice. p-values for SIRT6-tg vs. WT mice are shown and were derived from two-tailed log-rank calculations. Median lifespan for each genotype is shown in parentheses. b–d Neoplasia (b), cancer (c), and gastrointestinal adenoma (d) incidence at the age of 25 months. For (b–d), n = 16 mice per genotype, a mixture of both sexes. Two-tailed Fisher’s exact test; *p < 0.05 vs. WT. Exact p-values are reported in Supplementary Table 1. e Respiratory exchange ratio (RER) averaged over 3 days in young and old WT and SIRT6-tg mice from either sex measured every half-hour. Three-way ANOVA with repeated measures with age, genotype, and time as variables. Sidak’s post hoc; young WT (* in blue) or old SIRT6 (* in green) vs. old WT. n = 8 mice per group, except for young SIRT6 where n = 7 mice. f Spontaneous wheel running activity of males at the ages of 8 months (n = 8 mice per genotype) and 15 months (n = 9 mice for WT, SIRT1, SIRT6, n = 7 mice for SIRT1 + 6). Two-way ANOVA with time and genotype as variables. g Exercise ability on treadmill in young and old males. Two-way ANOVA with Sidak’s post hoc. n = 6 mice per group, except for young WT where n = 5 mice. *p < 0.05, **p < 0.01, ***p < 0.001. In (e) and (f), values are shown as mean ± SEM. In (g), box extends from the 25th to 75th percentiles, line in the middle of the box is the median and whiskers go down to the smallest value and up to the largest. For (e–g), exact p-values are reported in the Source Data file.CC BY · PubMed Central
Fig. 2 Serum metabolites associated with fasting and sirtuin overexpression. a Heatmap showing serum metabolites from key metabolic pathways that changed significantly either with dietary state (fed, and 4 h and 16 h of fasting), genotype, or both as measured in two-way ANOVA. Each square represents average metabolite abundance of 7 WT mice, 7 SIRT1-tg mice, 6 SIRT6-tg mice, and 5 SIRT1 + 6-tg mice. b–d Scatterplots of data from (a) showing the levels of BCAAs (b), glucose and the GNG precursors glycerol and lactate (c), and TCA cycle metabolites (d). For (b–d), n = 7 WT mice, n = 7 SIRT1-tg mice, n = 6 SIRT6-tg mice, and n = 5 SIRT1 + 6-tg mice. Box extends from the 25th to 75th percentiles, line in the middle of the box is the median and whiskers go down to the smallest value and up to the largest. Statistical analysis was performed using two-way ANOVA (time, genotype) with Dunnett’s post hoc correction. p-value for genotype or fasting effect is indicated in graphs, exact p-values are reported in Supplementary Table 4. *p < 0.05, **p < 0.01.
Fig. 2 Serum metabolites associated with fasting and sirtuin overexpression. a Heatmap showing serum metabolites from key metabolic pathways that changed significantly either with dietary state (fed, and 4 h and 16 h of fasting), genotype, or both as measured in two-way ANOVA. Each square represents average metabolite abundance of 7 WT mice, 7 SIRT1-tg mice, 6 SIRT6-tg mice, and 5 SIRT1 + 6-tg mice. b–d Scatterplots of data from (a) showing the levels of BCAAs (b), glucose and the GNG precursors glycerol and lactate (c), and TCA cycle metabolites (d). For (b–d), n = 7 WT mice, n = 7 SIRT1-tg mice, n = 6 SIRT6-tg mice, and n = 5 SIRT1 + 6-tg mice. Box extends from the 25th to 75th percentiles, line in the middle of the box is the median and whiskers go down to the smallest value and up to the largest. Statistical analysis was performed using two-way ANOVA (time, genotype) with Dunnett’s post hoc correction. p-value for genotype or fasting effect is indicated in graphs, exact p-values are reported in Supplementary Table 4. *p < 0.05, **p < 0.01.CC BY · PubMed Central
Fig. 3 SIRT6 overexpression blocks age-dependent normoglycemia and deterioration in gluconeogenesis capacity. a Blood glucose levels in young and old WT (blue) and SIRT6-tg (green) male mice at fed and fasted time points, as indicated in the graph. n = 10 young WT mice, n = 8 young SIRT6 mice, n = 7 old WT and old SIRT6 mice. b Same as (a), but in females. n = 8 young WT mice, n = 7 young SIRT6, old WT, and old SIRT6 mice. c Lactate tolerance test in 6 h-fasted male mice; n = 6 young WT, old WT, and old SIRT6 mice, n = 5 young SIRT6 mice. d Glycerol tolerance test in 6 h-fasted male mice; n = 6 mice per group. e Lactate tolerance test in 6 h-fasted female mice; n = 6 old WT, young SIRT6, and old SIRT6 mice, n = 7 young WT mice. f Glycerol tolerance test in 6 h-fasted female mice; n = 6 young WT and young SIRT6 mice, n = 8 old WT mice, n = 9 old SIRT6 mice. The area under the curve (AUC) for each tolerance test is shown on the right. For all panels, ages were 4–6 (young) and 22–24 (old) months. Statistical significance was calculated using three-way ANOVA with Sidak’s post hoc for line graphs, and two-way ANOVA with Fisher’s LSD method for bar graphs. Young WT (* in blue) or old SIRT6 (*in green) vs. old WT. In all panels, values are mean ± SEM. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Fig. 3 SIRT6 overexpression blocks age-dependent normoglycemia and deterioration in gluconeogenesis capacity. a Blood glucose levels in young and old WT (blue) and SIRT6-tg (green) male mice at fed and fasted time points, as indicated in the graph. n = 10 young WT mice, n = 8 young SIRT6 mice, n = 7 old WT and old SIRT6 mice. b Same as (a), but in females. n = 8 young WT mice, n = 7 young SIRT6, old WT, and old SIRT6 mice. c Lactate tolerance test in 6 h-fasted male mice; n = 6 young WT, old WT, and old SIRT6 mice, n = 5 young SIRT6 mice. d Glycerol tolerance test in 6 h-fasted male mice; n = 6 mice per group. e Lactate tolerance test in 6 h-fasted female mice; n = 6 old WT, young SIRT6, and old SIRT6 mice, n = 7 young WT mice. f Glycerol tolerance test in 6 h-fasted female mice; n = 6 young WT and young SIRT6 mice, n = 8 old WT mice, n = 9 old SIRT6 mice. The area under the curve (AUC) for each tolerance test is shown on the right. For all panels, ages were 4–6 (young) and 22–24 (old) months. Statistical significance was calculated using three-way ANOVA with Sidak’s post hoc for line graphs, and two-way ANOVA with Fisher’s LSD method for bar graphs. Young WT (* in blue) or old SIRT6 (*in green) vs. old WT. In all panels, values are mean ± SEM. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.CC BY · PubMed Central
Fig. 4 SIRT6 activates hepatic mitochondrial catabolic and anti-inflammatory pathways. RNA-seq was carried out on the liver of 6 months old WT and SIRT6-tg mice from either sex. a Principal component analysis was performed on all expressed genes. Each data point represents an individual mouse. b Gene Set Enrichment Analysis showing the top pathways up- or down-regulated in SIRT6-tg mice, sorted by normalized enrichment scores (NES). FDR q-values are shown in orange. Pathways related to catabolism and to inflammation are presented in blue and red, respectively. c Four metabolic pathways up-regulated in livers from male SIRT6-tg mice. In the upper part, each vertical black bar represents a gene in the gene set and its corresponding location in the sorted gene list. In the lower part, corresponding heatmaps show the expression values of the top genes in each pathway, which contribute most to the NES. High expression is colored in red and low expression in blue. Bold text indicates genes validated by qPCR in (e). d Ingenuity upstream regulator analysis common to males and females, based on genes differentially expressed between WT and SIRT6-tg mice. p-values for PPARα are shown, calculated by Fisher’s exact test. e Real-time PCR validation of gene expression from the indicated pathways, and mtDNA content in the livers of young and old WT and SIRT6-tg males. Two-way ANOVA with Fisher’s LSD method, asterisks indicate values significantly different between WT and SIRT6-tg at the same age. n = 8 mice per group, except for old SIRT6 for Coq10a (n = 6) and G6pc (n = 7), and for young WT and young SIRT6 for mtDNA content (n = 6). Mice ages are 6 and 25 months. RQ, relative quantity. Bars represent mean ± SEM. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Fig. 4 SIRT6 activates hepatic mitochondrial catabolic and anti-inflammatory pathways. RNA-seq was carried out on the liver of 6 months old WT and SIRT6-tg mice from either sex. a Principal component analysis was performed on all expressed genes. Each data point represents an individual mouse. b Gene Set Enrichment Analysis showing the top pathways up- or down-regulated in SIRT6-tg mice, sorted by normalized enrichment scores (NES). FDR q-values are shown in orange. Pathways related to catabolism and to inflammation are presented in blue and red, respectively. c Four metabolic pathways up-regulated in livers from male SIRT6-tg mice. In the upper part, each vertical black bar represents a gene in the gene set and its corresponding location in the sorted gene list. In the lower part, corresponding heatmaps show the expression values of the top genes in each pathway, which contribute most to the NES. High expression is colored in red and low expression in blue. Bold text indicates genes validated by qPCR in (e). d Ingenuity upstream regulator analysis common to males and females, based on genes differentially expressed between WT and SIRT6-tg mice. p-values for PPARα are shown, calculated by Fisher’s exact test. e Real-time PCR validation of gene expression from the indicated pathways, and mtDNA content in the livers of young and old WT and SIRT6-tg males. Two-way ANOVA with Fisher’s LSD method, asterisks indicate values significantly different between WT and SIRT6-tg at the same age. n = 8 mice per group, except for old SIRT6 for Coq10a (n = 6) and G6pc (n = 7), and for young WT and young SIRT6 for mtDNA content (n = 6). Mice ages are 6 and 25 months. RQ, relative quantity. Bars represent mean ± SEM. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.CC BY · PubMed Central
Fig. 5 Youthful-like hepatic TCA cycle, GNG, and redox metabolite levels in old SIRT6-tg. a Lists of statistically significant metabolites from each of the four comparisons shown were used to create Venn diagram. The number of significant metabolites in each comparison is shown in parentheses. b PCA of significantly changed metabolites. Each dot represents one biological replicate. c Heatmap of significantly changed metabolites. Each square represents average metabolite abundance of n = 5 mice per genotype. TSP, transsulfuration pathway; Y, young; O, old; WT, wild type; TG, transgenic. d–f Scatterplots showing metabolite levels of hepatic glycolysis/GNG (d), TCA cycle (e), and redox metabolism (f) pathways. n = 5 mice per genotype, each dot represents one mouse. g Expression of key NAD+ de novo synthesis genes in the liver of young and old WT and SIRT6-tg mice. Asterisks indicate values significantly different between WT and SIRT6-tg at the same age. n = 8 mice for young WT, young SIRT6, and old SIRT6 (for Haao in young SIRT6, n = 7); and n = 7 mice for old WT (for Nmnat1, n = 8), males. RQ, relative quantity. h Protein levels of NMNAT1 (n = 5 mice) and TDO2 (n = 7 mice) in livers of old WT and SIRT6-tg littermates. ImageJ quantification of NMNAT1 normalized to α-tubulin and TDO2 normalized to ponceau is shown in the right. For all panels, mice were at the ages of 5–7 months (young) and 20–24 months (old). Bars represent mean ± SEM. For (d–g) data were analyzed using two-way ANOVA, for (h) using two-tailed Student’s t-test. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Fig. 5 Youthful-like hepatic TCA cycle, GNG, and redox metabolite levels in old SIRT6-tg. a Lists of statistically significant metabolites from each of the four comparisons shown were used to create Venn diagram. The number of significant metabolites in each comparison is shown in parentheses. b PCA of significantly changed metabolites. Each dot represents one biological replicate. c Heatmap of significantly changed metabolites. Each square represents average metabolite abundance of n = 5 mice per genotype. TSP, transsulfuration pathway; Y, young; O, old; WT, wild type; TG, transgenic. d–f Scatterplots showing metabolite levels of hepatic glycolysis/GNG (d), TCA cycle (e), and redox metabolism (f) pathways. n = 5 mice per genotype, each dot represents one mouse. g Expression of key NAD+ de novo synthesis genes in the liver of young and old WT and SIRT6-tg mice. Asterisks indicate values significantly different between WT and SIRT6-tg at the same age. n = 8 mice for young WT, young SIRT6, and old SIRT6 (for Haao in young SIRT6, n = 7); and n = 7 mice for old WT (for Nmnat1, n = 8), males. RQ, relative quantity. h Protein levels of NMNAT1 (n = 5 mice) and TDO2 (n = 7 mice) in livers of old WT and SIRT6-tg littermates. ImageJ quantification of NMNAT1 normalized to α-tubulin and TDO2 normalized to ponceau is shown in the right. For all panels, mice were at the ages of 5–7 months (young) and 20–24 months (old). Bars represent mean ± SEM. For (d–g) data were analyzed using two-way ANOVA, for (h) using two-tailed Student’s t-test. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.CC BY · PubMed Central
Fig. 6 SIRT6 restores the significant reduction in hepatic lactate oxidation and contribution to GNG at old age. a Schematic showing fates of lactate carbons following injection of [U-13C]-lactate, after one round of the TCA cycle or flux through pyruvate carboxylase (PC, Pyr→OAA). White balls, 12C carbons. Colored balls, 13C carbons. Red, carbon flux from lactate to glucose via the TCA cycle. Yellow, carbon flux from lactate to glucose via PC. b Hepatic glucose isotopologues, 15 min after [U-13C]-lactate injection. c–e, Relative abundance of hepatic glucose [M + 2] and glucose [M + 3] (c), the TCA cycle metabolites αKG [M + 2], succinate [M + 2] and malate [M + 2] (d), and total pyruvate and pyruvate [M + 3] (e) 15 min after [U-13C]-lactate injection. Metabolite peak area values were normalized to total ion count. f, g Hepatic lactate [M + 3] / pyruvate [M + 3] peak area ratio (f), and ratio of total liver metabolite levels of lactate/pyruvate (g) 15 min after [U-13C]-lactate injection. For (b–g), mice were at the ages of 5–7 months (young) and 20–24 months (old). n = 5 mice; bars represent mean ± SEM. One-way ANOVA with Fisher’s LSD method. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. h Pyruvate tolerance test in 20 months old 6 h-fasted WT (n = 7 mice) and SIRT6-tg (n = 6 mice) male mice.
Fig. 6 SIRT6 restores the significant reduction in hepatic lactate oxidation and contribution to GNG at old age. a Schematic showing fates of lactate carbons following injection of [U-13C]-lactate, after one round of the TCA cycle or flux through pyruvate carboxylase (PC, Pyr→OAA). White balls, 12C carbons. Colored balls, 13C carbons. Red, carbon flux from lactate to glucose via the TCA cycle. Yellow, carbon flux from lactate to glucose via PC. b Hepatic glucose isotopologues, 15 min after [U-13C]-lactate injection. c–e, Relative abundance of hepatic glucose [M + 2] and glucose [M + 3] (c), the TCA cycle metabolites αKG [M + 2], succinate [M + 2] and malate [M + 2] (d), and total pyruvate and pyruvate [M + 3] (e) 15 min after [U-13C]-lactate injection. Metabolite peak area values were normalized to total ion count. f, g Hepatic lactate [M + 3] / pyruvate [M + 3] peak area ratio (f), and ratio of total liver metabolite levels of lactate/pyruvate (g) 15 min after [U-13C]-lactate injection. For (b–g), mice were at the ages of 5–7 months (young) and 20–24 months (old). n = 5 mice; bars represent mean ± SEM. One-way ANOVA with Fisher’s LSD method. Exact p-values are reported in the Source Data file. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. h Pyruvate tolerance test in 20 months old 6 h-fasted WT (n = 7 mice) and SIRT6-tg (n = 6 mice) male mice.CC BY · PubMed Central
Fig. 7 Enhanced adipose tissue-derived glycerol contributes to maintenance of normoglycemia in aged SIRT6-tg mice. a Lactate tolerance test in 22–24 months old liver-specific SIRT6-tg and appropriate littermate control male mice. Values are mean ± SEM of n = 6 WT mice, n = 5 lox mice, n = 9 Alb-cre mice and n = 5 liver SIRT6-tg mice. Two-way ANOVA showed no difference between liver SIRT6-tg and controls. Bars represent mean ± SEM. b Heatmap of GNG-related hepatic metabolites in 23–25 months old liver SIRT6-tg male mice and control littermates. Each square represents average metabolite abundance of n = 5 mice per genotype. See Supplementary Table 9 for source data. c Levels of the GNG precursors glycerol, lactate and pyruvate in the plasma of 6 h-fasted young (5–7 months) and old (20–24 months) mice, n = 5, males. Box extends from the 25th to 75th percentiles, line in the middle of the box is the median and whiskers go down to the smallest value and up to the largest. d Western blot and ImageJ quantification of HSL phosphorylation on Ser563 in white adipose tissue of 25 months old WT and SIRT6-tg littermates. n = 4 mice per genotype. Bars represent mean ± SEM, two-tailed Student’s t-test. For (c, d), exact p-values are reported in the Source Data file, *p < 0.05. e Schematic model depicting how SIRT6 improves longevity and healthspan by preserving NAD+ metabolism and energy production pathways in old age. Hepatic TCA cycle and GNG deteriorates during aging, leading to perturbed energy homeostasis and fasting glycemia in old age. SIRT6 overexpression increases hepatic energy production from fatty acids and AAs in the liver. This potentially spares GNG substrates, such as lactate and glycerol, whose shuttling during fasting from skeletal muscle and adipose tissue to the liver is increased by SIRT6. This cascade of events leads to preserved hepatic glucose production in old age. TG, triglyceride; HSL, hormone-sensitive lipase; DHAP, dihydroxyacetone phosphate; AAs, amino acids; Oxphos, oxidative phosphorylation.
Fig. 7 Enhanced adipose tissue-derived glycerol contributes to maintenance of normoglycemia in aged SIRT6-tg mice. a Lactate tolerance test in 22–24 months old liver-specific SIRT6-tg and appropriate littermate control male mice. Values are mean ± SEM of n = 6 WT mice, n = 5 lox mice, n = 9 Alb-cre mice and n = 5 liver SIRT6-tg mice. Two-way ANOVA showed no difference between liver SIRT6-tg and controls. Bars represent mean ± SEM. b Heatmap of GNG-related hepatic metabolites in 23–25 months old liver SIRT6-tg male mice and control littermates. Each square represents average metabolite abundance of n = 5 mice per genotype. See Supplementary Table 9 for source data. c Levels of the GNG precursors glycerol, lactate and pyruvate in the plasma of 6 h-fasted young (5–7 months) and old (20–24 months) mice, n = 5, males. Box extends from the 25th to 75th percentiles, line in the middle of the box is the median and whiskers go down to the smallest value and up to the largest. d Western blot and ImageJ quantification of HSL phosphorylation on Ser563 in white adipose tissue of 25 months old WT and SIRT6-tg littermates. n = 4 mice per genotype. Bars represent mean ± SEM, two-tailed Student’s t-test. For (c, d), exact p-values are reported in the Source Data file, *p < 0.05. e Schematic model depicting how SIRT6 improves longevity and healthspan by preserving NAD+ metabolism and energy production pathways in old age. Hepatic TCA cycle and GNG deteriorates during aging, leading to perturbed energy homeostasis and fasting glycemia in old age. SIRT6 overexpression increases hepatic energy production from fatty acids and AAs in the liver. This potentially spares GNG substrates, such as lactate and glycerol, whose shuttling during fasting from skeletal muscle and adipose tissue to the liver is increased by SIRT6. This cascade of events leads to preserved hepatic glucose production in old age. TG, triglyceride; HSL, hormone-sensitive lipase; DHAP, dihydroxyacetone phosphate; AAs, amino acids; Oxphos, oxidative phosphorylation.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 70

  1. Partridge, L., Deelen, J. & Slagboom, P. E. Facing up to the global challenges of ageing. Nature 561, 45–56 (2018). · DOI · Paper page
  2. Osborne, T. B., Mendel, L. B. & Ferry, E. L. The effect of retardation of growth upon the breeding period and duration of life of rats. Science 45, 294–295 (1917). · DOI
  3. Di Francesco, A., Di Germanio, C., Bernier, M. & de Cabo, R. A time to fast. Science 362, 770–775 (2018). · DOI
  4. Bratic, I. & Trifunovic, A. Mitochondrial energy metabolism and ageing. Biochim. Biophys. Acta 1797, 961–967 (2010). · DOI
  5. Wilson, C. H. et al. Age-related proteostasis and metabolic alterations in Caspase-2-deficient mice. Cell Death Dis. 6, e1615–e1615 (2015). · DOI
  6. Ruetenik, A. & Barrientos, A. Dietary restriction, mitochondrial function and aging: from yeast to humans. Biochim. Biophys. Acta 1847, 1434–1447 (2015). · DOI · Paper page
  7. Rothman, D., Magnusson, I., Katz, L., Shulman, R. & Shulman, G. Quantitation of hepatic glycogenolysis and gluconeogenesis in fasting humans with 13C NMR. Science 254, 573–576 (1991). · DOI · Paper page
  8. Petersen, M. C., Vatner, D. F. & Shulman, G. I. Regulation of hepatic glucose metabolism in health and disease. Nat. Rev. Endocrinol. 13, 572–587 (2017). · DOI
  9. Satrústegui, J., Cuezva, J. M. & Machado, A. Increased basal gluconeogenesis in the aged rat.FEBS Lett. 197, 159–163 (1986). · DOI
  10. Sumida, K. D., Crandall, S. C., Chadha, P. L. & Qureshi, T. Hepatic gluconeogenic capacity from various precursors in young versus old rats. Metabolism 51, 876–880 (2002). · DOI
  11. Horn, D. B., Podolin, D. A., Friedman, J. E., Scholnick, D. A. & Mazzeo, R. S. Alterations in key gluconeogenic regulators with age and endurance training. Metabolism 46, 414–419 (1997). · DOI
  12. Podolin, D. A., Gleeson, T. T. & Mazzeo, R. S. Hormonal regulation of hepatic gluconeogenesis: influence of age and training. Am. J. Physiol. 270, R365–R372 (1996).
  13. Guarente, L. Sirtuins: methods and protocols. In Methods in Molecular Biology Vol. 1077 (ed. Hirschey, M.D.) (Humana Press, 2013).
  14. Kanfi, Y. et al. Regulation of SIRT1 protein levels by nutrient availability. FEBS Lett. 582, 2417–2423 (2008). · DOI
  15. Kanfi, Y. et al. Regulation of SIRT6 protein levels by nutrient availability. FEBS Lett. 582, 543–548 (2008). · DOI
  16. Cohen, H. Y. Calorie restriction promotes mammalian cell survival by inducing the SIRT1 deacetylase. Science 305, 390–392 (2004). · DOI · Paper page
  17. Cheng, H.-L. et al. Developmental defects and p53 hyperacetylation in Sir2 homolog (SIRT1)-deficient mice. Proc. Natl Acad. Sci. USA 100, 10794–10799 (2003). · DOI · Paper page
  18. McBurney, M. W. et al. The mammalian SIR2 protein has a role in embryogenesis and gametogenesis. Mol. Cell. Biol. 23, 38–54 (2003). · DOI · Paper page
  19. Mostoslavsky, R. et al. Genomic instability and aging-like phenotype in the absence of mammalian SIRT6. Cell 124, 315–329 (2006). · DOI · Paper page
  20. Naiman, S. & Cohen, H. Y. Role for the longevity protein SIRT6 in primate development. Nature 560, 559–560 (2018). · DOI
  21. Satoh, A. et al. Sirt1 extends life span and delays aging in mice through the regulation of Nk2 homeobox 1 in the DMH and LH. Cell Metab. 18, 416–430 (2013). · DOI
  22. Roichman, A. et al. SIRT6 overexpression improves various aspects of mouse healthspan. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 72, 603–615 (2017).
  23. Kugel, S. & Mostoslavsky, R. Chromatin and beyond: the multitasking roles for SIRT6. Trends Biochem. Sci. 39, 72–81 (2014). · DOI
  24. Zhong, L. et al. The histone deacetylase Sirt6 regulates glucose homeostasis via Hif1alpha. Cell 140, 280–293 (2010). · DOI · Paper page
  25. Kim, H. et al. Hepatic-specific disruption of SIRT6 in mice results in fatty liver formation due to enhanced glycolysis and triglyceride synthesis. Cell Metab. 12, 224–236 (2010). · DOI
  26. Naiman, S. et al. SIRT6 promotes hepatic beta-oxidation via activation of PPARα. Cell Rep. 29, 4127–4143.e8 (2019). · DOI
  27. Dominy, J. E. et al. The deacetylase Sirt6 activates the acetyltransferase GCN5 and suppresses hepatic gluconeogenesis. Mol. Cell 48, 900–913 (2012). · DOI
  28. Zhang, P. et al. Tumor suppressor p53 cooperates with SIRT6 to regulate gluconeogenesis by promoting FoxO1 nuclear exclusion. Proc. Natl Acad. Sci. USA 111, 10684–10689 (2014). · DOI
  29. Harris, T. B. et al. Changes in weight at the end of life: characterizing weight loss by time to death in a cohort study of older men. Am. J. Epidemiol. 172, 558–565 (2010). · DOI
  30. Guo, W., Li, M. & Bhasin, S. Testosterone supplementation improves anemia in aging male mice. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 69A, 505–513 (2014). · DOI
  31. Goodnough, L. T. & Schrier, S. L. Evaluation and management of anemia in the elderly. Am. J. Hematol. 89, 88–96 (2014). · DOI
  32. López-Otín, C., Galluzzi, L., Freije, J. M. P., Madeo, F. & Kroemer, G. Metabolic control of longevity. Cell 166, 802–821 (2016). · DOI · Paper page
  33. Liu, H., Graber, T. G., Ferguson-Stegall, L. & Thompson, L. V. Clinically relevant frailty index for mice. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 69, 1485–1491 (2014). · DOI
  34. Rui, L. Energy metabolism in the liver.Compr. Physiol. 4, 177–197 (2014). · DOI
  35. Rush, M. J. P. et al. Caloric restriction engages hepatic RNA processing mechanisms in rhesus monkeys. Cell Metab. 27, 677–688.e5 (2018). · DOI
  36. Tomaru, U. et al. Decreased proteasomal activity causes age-related phenotypes and promotes the development of metabolic abnormalities. Am. J. Pathol. 180, 963–972 (2012). · DOI · Paper page
  37. Nguyen, N. N. et al. Proteasome β5 subunit overexpression improves proteostasis during aging and extends lifespan in Drosophila melanogaster. Sci. Rep. 9, 3170 (2019). · DOI
  38. Iacobazzi, V. & Infantino, V. Citrate – new functions for an old metabolite. Biol. Chem. 395, 387–399 (2014). · DOI
  39. Honda, Y., Tanaka, M. & Honda, S. Trehalose extends longevity in the nematode Caenorhabditis elegans. Aging Cell 9, 558–569 (2010). · DOI
  40. Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017). · DOI · Paper page
  41. Williamson, D. H., Lund, P. & Krebs, H. A. The redox state of free nicotinamide-adenine dinucleotide in the cytoplasm and mitochondria of rat liver. Biochem. J. 103, 514–527 (1967). · DOI · Paper page
  42. Chen, X., Iqbal, N. & Boden, G. The effects of free fatty acids on gluconeogenesis and glycogenolysis in normal subjects. J. Clin. Invest. 103, 365–372 (1999). · DOI · Paper page
  43. Kirkwood, T. B. L. & Austad, S. N. Why do we age? Nature 408, 233–238 (2000). · DOI · Paper page
  44. Xiong, X. et al. Fabp4-Cre-mediated Sirt6 deletion impairs adipose tissue function and metabolic homeostasis in mice. J. Endocrinol. 233, 307–314 (2017). · DOI
  45. Kuang, J. et al. Fat-specific Sirt6 ablation sensitizes mice to high-fat diet-induced obesity and insulin resistance by inhibiting lipolysis. Diabetes 66, 1159–1171 (2017). · DOI
  46. Yao, L. et al. Cold-inducible SIRT6 regulates thermogenesis of brown and beige fat. Cell Rep. 20, 641–654 (2017). · DOI
  47. Hagopian, K. Caloric restriction increases gluconeogenic and transaminase enzyme activities in mouse liver. Exp. Gerontol. 38, 267–278 (2003). · DOI
  48. Selman, C. et al. Coordinated multitissue transcriptional and plasma metabonomic profiles following acute caloric restriction in mice. Physiol. Genomics 27, 187–200 (2006). · DOI
  49. Exton, J. H. Gluconeogenesis. Metabolism 21, 945–990 (1972). · DOI
  50. Brooks, G. A. The precious few grams of glucose during exercise. Int. J. Mol. Sci. 21, 1–19 (2020). · DOI
  51. Korol, D. L. & Gold, P. E. Glucose, memory, and aging. Am. J. Clin. Nutr. 67, 764S–771S (1998). · DOI
  52. Spindler, S. R. Calorie restriction enhances the expression of key metabolic enzymes associated with protein renewal during aging. Ann. NY Acad. Sci. 928, 296–304 (2006). · DOI
  53. Yuan, Y. et al. Enhanced energy metabolism contributes to the extended life span of calorie-restricted Caenorhabditis elegans. J. Biol. Chem. 287, 31414–31426 (2012). · DOI
  54. Yuan, Y. et al. Reciprocal changes in phosphoenolpyruvate carboxykinase and pyruvate kinase with age are a determinant of aging in Caenorhabditis elegans. J. Biol. Chem. 291, 1307–1319 (2016). · DOI
  55. Baker, E. J., Miles, E. A., Burdge, G. C., Yaqoob, P. & Calder, P. C. Metabolism and functional effects of plant-derived omega-3 fatty acids in humans. Prog. Lipid Res. 64, 30–56 (2016). · DOI
  56. Gubbels Bupp, M. R. Sex, the aging immune system, and chronic disease. Cell. Immunol. 294, 102–110 (2015). · DOI
  57. Fiehn, O. et al. Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J. 53, 691–704 (2008). · DOI
  58. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). · DOI · Paper page
  59. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). · DOI · Paper page
  60. Krämer, A., Green, J., Pollard, J. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014). · DOI · Paper page
  61. Mackay, G.M., Zheng, L., van den Broek, N.J.F. & Gottlieb, E. Analysis of cell metabolism using LC-MS and isotope tracers.Methods Enzymol. 561, 171–196 (2015). · DOI
  62. Wessel, D. & Flügge, U. I. A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal. Biochem. 138, 141–143 (1984). · DOI
  63. Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011). · DOI
  64. Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002). · DOI
  65. Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003). · DOI
  66. Herbrich, S. M. et al. Statistical inference from multiple iTRAQ experiments without using common reference standards. J. Proteome Res. 12, 594–604 (2013). · DOI
  67. Kammers, K., Cole, R. N., Tiengwe, C. & Ruczinski, I. Detecting significant changes in protein abundance. EuPA Open Proteom. 7, 11–19 (2015). · DOI
  68. Hayter, A. J. The maximum familywise error rate of Fisher’s least significant difference test. J. Am. Stat. Assoc. 81, 1000–1004 (1986). · DOI
  69. Chong, J. et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486–W494 (2018). · DOI
  70. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019). · DOI

Cited by

Sorted by citations

See all 214

Metadata

Authors
Roichman, A., Elhanati, S., Aon, M. A., Abramovich, I., Di Francesco, A., Shahar, Y., Avivi, M. Y., Shurgi, M., Rubinstein, A., Wiesner, Y., Shuchami, A., Petrover, Z., Lebenthal-Loinger, I., Yaron, O., Lyashkov, A., Ubaida-Mohien, C., Kanfi, Y., Lerrer, B., Fernández-Marcos, P. J., Serrano, M., Gottlieb, E., de Cabo, R., & Cohen, H. Y.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-05-28
Volume
12
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-021-23545-7
PMC
PMC8163764
Cited by
214
References
20
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review